In [1]:
# Import necessary libraries for data analysis and visualization
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
In [2]:
# Load the dataset 
df=pd.read_csv('Shark Tank India Dataset.csv')
# Display the first few rows of the dataset
df.head()
Out[2]:
episode_number pitch_number brand_name idea deal pitcher_ask_amount ask_equity ask_valuation deal_amount deal_equity ... ashneer_deal anupam_deal aman_deal namita_deal vineeta_deal peyush_deal ghazal_deal total_sharks_invested amount_per_shark equity_per_shark
0 1 1 BluePine Industries Frozen Momos 1 50.0 5.0 1000.00 75.0 16.00 ... 1 0 1 0 1 0 0 3 25.0 5.333333
1 1 2 Booz scooters Renting e-bike for mobility in private spaces 1 40.0 15.0 266.67 40.0 50.00 ... 1 0 0 0 1 0 0 2 20.0 25.000000
2 1 3 Heart up my Sleeves Detachable Sleeves 1 25.0 10.0 250.00 25.0 30.00 ... 0 1 0 0 1 0 0 2 12.5 15.000000
3 2 4 Tagz Foods Healthy Potato Chips 1 70.0 1.0 7000.00 70.0 2.75 ... 1 0 0 0 0 0 0 1 70.0 2.750000
4 2 5 Head and Heart Brain Development Course 0 50.0 5.0 1000.00 0.0 0.00 ... 0 0 0 0 0 0 0 0 0.0 0.000000

5 rows × 28 columns

In [3]:
# Get information about the dataset
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 117 entries, 0 to 116
Data columns (total 28 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   episode_number         117 non-null    int64  
 1   pitch_number           117 non-null    int64  
 2   brand_name             117 non-null    object 
 3   idea                   117 non-null    object 
 4   deal                   117 non-null    int64  
 5   pitcher_ask_amount     117 non-null    float64
 6   ask_equity             117 non-null    float64
 7   ask_valuation          117 non-null    float64
 8   deal_amount            117 non-null    float64
 9   deal_equity            117 non-null    float64
 10  deal_valuation         117 non-null    float64
 11  ashneer_present        117 non-null    int64  
 12  anupam_present         117 non-null    int64  
 13  aman_present           117 non-null    int64  
 14  namita_present         117 non-null    int64  
 15  vineeta_present        117 non-null    int64  
 16  peyush_present         117 non-null    int64  
 17  ghazal_present         117 non-null    int64  
 18  ashneer_deal           117 non-null    int64  
 19  anupam_deal            117 non-null    int64  
 20  aman_deal              117 non-null    int64  
 21  namita_deal            117 non-null    int64  
 22  vineeta_deal           117 non-null    int64  
 23  peyush_deal            117 non-null    int64  
 24  ghazal_deal            117 non-null    int64  
 25  total_sharks_invested  117 non-null    int64  
 26  amount_per_shark       117 non-null    float64
 27  equity_per_shark       117 non-null    float64
dtypes: float64(8), int64(18), object(2)
memory usage: 25.7+ KB
In [4]:
# Get the shape of the dataset
df.shape
Out[4]:
(117, 28)
In [5]:
# Display the last few rows of the dataset
df.tail()
Out[5]:
episode_number pitch_number brand_name idea deal pitcher_ask_amount ask_equity ask_valuation deal_amount deal_equity ... ashneer_deal anupam_deal aman_deal namita_deal vineeta_deal peyush_deal ghazal_deal total_sharks_invested amount_per_shark equity_per_shark
112 34 113 Green Protein Plant-Based Protein 0 60.0 2.0 3000.0 0.0 0.0 ... 0 0 0 0 0 0 0 0 0.0 0.0
113 34 114 On2Cook Fastest Cooking Device 0 100.0 1.0 10000.0 0.0 0.0 ... 0 0 0 0 0 0 0 0 0.0 0.0
114 35 115 Jain Shikanji Lemonade 1 40.0 8.0 500.0 40.0 30.0 ... 1 1 1 0 1 0 0 4 10.0 7.5
115 35 116 Woloo Washroom Finder 0 50.0 4.0 1250.0 0.0 0.0 ... 0 0 0 0 0 0 0 0 0.0 0.0
116 35 117 Elcare India Carenting for Elders 0 100.0 2.5 4000.0 0.0 0.0 ... 0 0 0 0 0 0 0 0 0.0 0.0

5 rows × 28 columns

In [6]:
# Generate descriptive statistics of the dataset
df.describe()
Out[6]:
episode_number pitch_number deal pitcher_ask_amount ask_equity ask_valuation deal_amount deal_equity deal_valuation ashneer_present ... ashneer_deal anupam_deal aman_deal namita_deal vineeta_deal peyush_deal ghazal_deal total_sharks_invested amount_per_shark equity_per_shark
count 117.000000 117.000000 117.000000 117.000000 117.000000 117.000000 117.000000 117.000000 117.000000 117.000000 ... 117.000000 117.000000 117.000000 117.000000 117.000000 117.000000 117.000000 117.000000 117.000000 117.000000
mean 18.735043 59.000000 0.555556 319.854709 5.188034 3852.462479 31.982915 8.963504 467.104872 0.837607 ... 0.179487 0.205128 0.239316 0.188034 0.128205 0.230769 0.059829 1.230769 18.132481 5.583590
std 10.070778 33.919021 0.499041 2767.842777 3.892121 11931.601957 36.687391 13.106769 919.988864 0.370397 ... 0.385410 0.405532 0.428501 0.392420 0.335756 0.423137 0.238190 1.410457 23.588682 10.803799
min 1.000000 1.000000 0.000000 0.001010 0.250000 0.010000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 10.000000 30.000000 0.000000 45.000000 2.500000 666.670000 0.000000 0.000000 0.000000 1.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 19.000000 59.000000 1.000000 50.000000 5.000000 1250.000000 25.000000 3.000000 100.000000 1.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 10.000000 1.250000
75% 27.000000 88.000000 1.000000 80.000000 7.500000 2857.140000 50.000000 15.000000 500.000000 1.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000 25.000000 6.000000
max 35.000000 117.000000 1.000000 30000.000000 25.000000 120000.000000 150.000000 75.000000 6666.670000 1.000000 ... 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 5.000000 100.000000 75.000000

8 rows × 26 columns

In [7]:
# Check for missing values in the dataset
df.isnull().sum()
Out[7]:
episode_number           0
pitch_number             0
brand_name               0
idea                     0
deal                     0
pitcher_ask_amount       0
ask_equity               0
ask_valuation            0
deal_amount              0
deal_equity              0
deal_valuation           0
ashneer_present          0
anupam_present           0
aman_present             0
namita_present           0
vineeta_present          0
peyush_present           0
ghazal_present           0
ashneer_deal             0
anupam_deal              0
aman_deal                0
namita_deal              0
vineeta_deal             0
peyush_deal              0
ghazal_deal              0
total_sharks_invested    0
amount_per_shark         0
equity_per_shark         0
dtype: int64
In [8]:
# Count the number of companies that received investment and those that didn't
got_in = df[df['deal_amount'] > 0.0].shape
not_in = df[df['deal_amount'] == 0.0].shape
# Print the counts of companies that received and didn't receive investment
print("The number of companies who got the investment: ", got_in[0])
print("The number of companies who didn't got the investment: ", not_in[0])
print("\n---------\n")
# Visualize the distribution of companies that got investment vs. those that didn't using a pie chart
list1=["Got Investment", "Not Received Investment"]
plt.pie([got_in[0], not_in[0]], labels=list1, autopct='%.1f%%')
plt.title('Number of companies which got invextment or not: ')
plt.show()

The number of companies who got the investment:  65
The number of companies who didn't got the investment:  52

---------
In [9]:
# Filter companies where all sharks invested
df[df["total_sharks_invested"]==5]
Out[9]:
episode_number pitch_number brand_name idea deal pitcher_ask_amount ask_equity ask_valuation deal_amount deal_equity ... ashneer_deal anupam_deal aman_deal namita_deal vineeta_deal peyush_deal ghazal_deal total_sharks_invested amount_per_shark equity_per_shark
15 6 16 Skippi Pops Ice-Pops 1 45.0 5.0 900.0 100.0 15.0 ... 1 1 1 1 1 0 0 5 20.0 3.0
49 17 50 Find Your Kicks India Sneaker Resale 1 50.0 10.0 500.0 50.0 25.0 ... 1 1 1 1 0 1 0 5 10.0 5.0
63 20 64 IN A CAN Can Cocktails 1 50.0 2.0 2500.0 100.0 10.0 ... 1 1 1 1 0 1 0 5 20.0 2.0
79 25 80 Sunfox Technologies Portable ECG Device 1 100.0 2.0 5000.0 100.0 6.0 ... 0 1 0 1 1 1 1 5 20.0 1.2

4 rows × 28 columns

In [10]:
#Total Shark Deal:
# Visualize the brands involved in five-shark deals and their total investment using a bar chart
total_shark=df[df["total_sharks_invested"]==5]
figure=px.bar(total_shark, x='brand_name', y='deal_amount',title="Five Shark Deal Brands and the total investment:",text_auto=True, color='pitcher_ask_amount',
                    template="plotly_dark")
figure.show()
In [11]:
#Highest Pitch Ask Amount
# Filter companies with the highest pitch ask amount
high=df[df["pitcher_ask_amount"]>100]
# Visualize the brand with the highest pitch ask amount using a bar chart
figure=px.bar(high, x='brand_name', y='pitcher_ask_amount',title="Highest Pitch Ask Amount:",text_auto=True, color='deal_amount',
                    template="plotly_dark")
figure.show()
In [12]:
#Ask Equity and Deal Equity of Highest Pitch Ask AMount Brand
# Visualize the ask equity and deal equity of the brand with the highest pitch ask amount
figure=px.bar(high, x='brand_name', y='ask_equity',title="Ask Equity & Deal Equity of Highest Pitch Ask Amount Brand:",text_auto=True, color='deal_equity',
                    template="plotly_dark")
figure.show()
In [13]:
#Least Pitch Amount Asked 
# Filter companies with the lowest pitch amount asked
low=df[df["pitcher_ask_amount"]<20]
# Visualize the brand with the lowest pitch ask amount using a bar chart
figure=px.bar(low, x='brand_name', y='pitcher_ask_amount',title="Lowest Pitch Ask Amount:",text_auto=True, color='deal_amount',
                    template="plotly_dark")
figure.show()
In [14]:
#No. of brands the sharks have invested:
# Calculate the number of deals each shark has invested in
num_deal_shark=[df.ashneer_deal.sum(), df.anupam_deal.sum(), df.aman_deal.sum(), df.namita_deal.sum(), df.vineeta_deal.sum(), df.peyush_deal.sum(), df.ghazal_deal.sum()]
all_sharks=["Ashneer", "Anupam", "Aman", "Namita", "Vineeta", "Peyush", "Ghazal"]
# Visualize the number of deals done by each individual shark using a bar chart
figure=px.bar(total_shark, x=all_sharks, y=num_deal_shark,title="Number of deals done by individual shark:",color=all_sharks,text_auto=True,
                    template="plotly_dark")
figure.show()
In [15]:
#Above 50% Equity taken by the shark in which brand?:
# Filter companies where a shark has taken above 50% equity
df_equity=df[df['equity_per_shark']>=50]
# Visualize the brands where a shark has taken above 50% equity using a bar chart
figure=px.bar(df_equity, x='brand_name', y='equity_per_shark',title="Above 50% Equity taken by the shark in which brand:",text_auto=True, color='deal_amount',
                    template="plotly_dark")
figure.show()
In [16]:
# How much equity is offered by entrepreneurs to the investors in the deals (Avg)
# Calculate the average equity offered by entrepreneurs to investors in deals
average_equity = df['deal_equity'].mean()
print(f"The average equity offered by entrepreneurs to investors on Shark Tank India is: {average_equity:.2f}")

The average equity offered by entrepreneurs to investors on Shark Tank India is: 8.96
In [17]:
# Average amount invested in the deals made
average_investment = df['deal_amount'].mean()
print(f"The average amount invested in the deals made on Shark Tank India is: {average_investment}")

The average amount invested in the deals made on Shark Tank India is: 31.982915042735044
In [18]:
# Correlation between the pitchers asking valuation and the actual deal valuation in the deals
# Visualize the correlation between pitchers' asking valuation and actual deal valuation
figure = px.scatter(df, x='ask_valuation', y='deal_valuation',
                 labels={'ask_valuation': "Pitchers' Asking Valuation",
                         'deal_valuation': "Actual Deal Valuation"},
                 title="Correlation between Pitchers' Asking Valuation and Actual Deal Valuation",
                 color='deal_amount',
                 color_continuous_scale='Viridis', 
                 hover_name='brand_name'
                 )

figure.update_layout(template='plotly_dark')

figure.show()
In [19]:
# Success Rate of Deals Made by Shark
# Calculate and visualize the success rate of deals made by each shark
total_deals_by_shark = df[['ashneer_deal', 'anupam_deal', 'aman_deal', 'namita_deal', 'vineeta_deal', 'peyush_deal', 'ghazal_deal']].sum()
successful_deals_by_shark = df[df['deal'] == 1][['ashneer_deal', 'anupam_deal', 'aman_deal', 'namita_deal', 'vineeta_deal', 'peyush_deal', 'ghazal_deal']].sum()
success_rate_by_shark = (successful_deals_by_shark / total_deals_by_shark) * 100
bar_colors = ['rgb(65,105,225)', 'rgb(144,238,144)', 'rgb(218,165,32)', 'rgb(199,21,133)', 'rgb(255,140,0)', 'rgb(75,0,130)', 'rgb(255,0,0)']

figure = go.Figure(data=go.Bar(
    x=success_rate_by_shark.index,
    y=success_rate_by_shark,
    marker=dict(color=bar_colors),
    hovertemplate='Success Rate: %{y:.2f}%<br><extra></extra>'
))

figure.update_layout(
    title="Success Rate of Deals Made by Shark",
    xaxis_title="Shark",
    yaxis_title="Success Rate (%)",
    template='plotly_dark'
)

figure.show()
In [20]:
# Visualize the relationship between investment amount, equity offered, and deal valuation
figure = go.Figure(data=[go.Scatter3d(
    x=df['pitcher_ask_amount'],
    y=df['ask_equity'],
    z=df['deal_amount'],
    mode='markers',
    marker=dict(
        size=8,
        color=df['deal_valuation'],
        colorscale='Blues',
        colorbar=dict(title='Deal Valuation'),
        showscale=True
    ),
    text=df['brand_name'],
    hovertemplate='Brand: %{text}<br>Investment Amount: %{x}<br>Equity Offered: %{y}<br>Deal Valuation: %{z}<extra></extra>'
)])

figure.update_layout(
    title="Investment Amount, Equity Offered, and Deal Valuation on Shark Tank India",
    scene=dict(
        xaxis_title="Investment Amount",
        yaxis_title="Equity Offered",
        zaxis_title="Deal Amount"
    ),
    template='plotly_dark'
)

figure.show()
In [21]:
# Visualize the relationship between pitch valuation and equity offered by entrepreneurs
figure = px.scatter(df, x='ask_valuation', y='ask_equity',
                 labels={'ask_valuation': "Pitch Valuation",
                         'ask_equity': "Equity Offered"},
                 title="Relationship between Pitch Valuation and Equity Offered on Shark Tank India",
                 trendline="ols", 
                 hover_name='brand_name')

figure.update_layout(template='plotly_dark')

figure.show()
In [22]:
# Visualize the relationship between pitch valuation, equity offered, and deal amount
figure = go.Figure(data=[go.Scatter3d(
    x=df['ask_valuation'],
    y=df['ask_equity'],
    z=df['deal_amount'],
    mode='markers',
    marker=dict(
        size=8,
        color=df['deal_amount'],
        colorscale='Viridis',
        opacity=0.8
    ),
    hovertemplate='<b>Brand:</b> %{text}<br>'
                  '<b>Pitch Valuation:</b> %{x}<br>'
                  '<b>Equity Offered:</b> %{y}<br>'
                  '<b>Deal Amount:</b> %{z}<br><extra></extra>',
    text=df['brand_name']
)])

figure.update_layout(
    title="Relationship between Pitch Valuation, Equity Offered, and Deal Amount",
    scene=dict(
        xaxis_title="Pitch Valuation",
        yaxis_title="Equity Offered",
        zaxis_title="Deal Amount"
    ),
    template='plotly_dark'
)

figure.show()
In [23]:
# Comparision of Successful Pitches and Unsuccessful Pitches
successful_pitches = df[df['deal'] == 1]
unsuccessful_pitches = df[df['deal'] == 0]
successful_mean_valuation = successful_pitches['ask_valuation'].mean()
unsuccessful_mean_valuation = unsuccessful_pitches['ask_valuation'].mean()

figure = px.bar(x=['Successful Pitches', 'Unsuccessful Pitches'],
             y=[successful_mean_valuation, unsuccessful_mean_valuation],
             color=['Successful Pitches', 'Unsuccessful Pitches'],
             color_discrete_sequence=['rgb(65,105,225)', 'rgb(144,238,144)'],
             labels={'x': '', 'y': 'Average Valuation'},
             template='plotly_dark')

figure.update_layout(title='Average Valuation of Successful vs Unsuccessful Pitches')

figure.show()
In [ ]: